Bottom Line:
As a result, such metrics could be difficult to apply to real social networks.From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms.Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure.

Affiliation: Levich Institute and Physics Department, City College of New York, New York, NY, United States of America.

ABSTRACTMost centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

pone.0136831.g002: Collective action predicts influential spreaders more reliably than other social mechanisms.When spreading originates in people with (aα, aca), the relative epidemic size M(aα, aca) for the QXF with (a) aexc, (b) abal, and (c) ash, (d-f) QXG, (g-i) POK, and (j-l) LJ networks. Collective action aca predicts the epidemic influence more reliably than the other social interactions when we compare for people with the same degree.

Mentions:
To compare the influence of each social mechanisms in the spreading process, we study the average size ΔM infected in an epidemic originating at people i with a given (, , , ). The average infected population over all the origins with the same pair of (aα, aβ) isΔM=∑i∈W(aα,aβ)ΔMiN(aα,aβ),(4)where W(aα, aβ) is the union of all nodes with (aα, aβ) and N(aα, aβ) is the number of nodes with (aα, aβ). In Fig 2, we find that ΔM increases with increasing aca regardless with the other social mechanisms for all tested networks. This clear pattern suggests that aca predicts the epidemic influence more reliably than the other social interactions when we compare for people with the same degree.

pone.0136831.g002: Collective action predicts influential spreaders more reliably than other social mechanisms.When spreading originates in people with (aα, aca), the relative epidemic size M(aα, aca) for the QXF with (a) aexc, (b) abal, and (c) ash, (d-f) QXG, (g-i) POK, and (j-l) LJ networks. Collective action aca predicts the epidemic influence more reliably than the other social interactions when we compare for people with the same degree.

Mentions:
To compare the influence of each social mechanisms in the spreading process, we study the average size ΔM infected in an epidemic originating at people i with a given (, , , ). The average infected population over all the origins with the same pair of (aα, aβ) isΔM=∑i∈W(aα,aβ)ΔMiN(aα,aβ),(4)where W(aα, aβ) is the union of all nodes with (aα, aβ) and N(aα, aβ) is the number of nodes with (aα, aβ). In Fig 2, we find that ΔM increases with increasing aca regardless with the other social mechanisms for all tested networks. This clear pattern suggests that aca predicts the epidemic influence more reliably than the other social interactions when we compare for people with the same degree.

Bottom Line:
As a result, such metrics could be difficult to apply to real social networks.From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms.Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure.

Affiliation:
Levich Institute and Physics Department, City College of New York, New York, NY, United States of America.

ABSTRACTMost centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.